Google Cloud Data & AI services

Short overview

Leverage the data in your company for innovation and growth with PCG's "Data to Value" approach. Benefit from the power of the Google Cloud to manage and analyse large data sets, implement AI models and successfully implement data-driven use cases. Our data and AI solutions are tailored to your needs. For a successful data foundation, for innovative (Gen) AI applications and a secure future shaped by data-driven decisions.

In order to generate real business value from data, you need not only a good and structured data foundation, but also a data hub in which the data is consolidated and managed. The Google Cloud Data Hub from PCG is the central hub between your database and your downstream initiatives such as generative AI, machine learning, dashboarding and business intelligence.

The central pillars of PCG's Google Cloud "Data to Value" approach: Data Foundation, Data Hub and Data-Driven Value Generation:

Data Foundation

A successful data foundation is crucial to the success of your data-based initiatives and projects. Our experienced data architects support you in setting up your data foundation. From the initial screening of your data architecture to the development of smart data pipelines and data storages as well as data modelling, data access and governance.

Data Architecture Screening Workshop

As part of our workshop, PCG's Google Cloud experts will examine your current data landscape, record your requirements and define a customized data architecture as the foundation for your data strategy on Google Cloud. In addition to topics such as compliance and GDPR, this also includes change-data capture, data source identification and the definition of further use cases. Based on this, we create your data roadmap and provide estimates for cloud usage and implementation costs.

Smart Data Pipelines

We develop smart data pipelines that are used to extract data from various sources and link it seamlessly to a central repository in the Google Cloud. To do so, we rely on native Google Cloud services. Our Smart Data Pipelines take on the tasks of data transformation, exception handling and orchestration. They ensure a continuous and uninterrupted flow of data from various operational databases, SaaS solutions such as Salesforce and the SAP ecosystem, among others.

Data Store (DWH, Data Lake, Data Mesh)

We configure data warehouses (DWH) and data marts using the native functions of the Google Cloud. Alternatively, we design and set up data lakes with different data maturity zones or introduce a data mesh architecture for separate data producer and consumer teams in your company.

Data Modelling

We support you with data modeling. This includes the creation of dimension and fact tables for data warehouses and data marts, which are derived from a data vault, for example. For data lakes and data meshes, we ensure optimal table designs, such as Delta, Hudi and Iceberg, and take partitioning and compaction into account.

Data Access and Data Governance

We support you in the areas of data access and compliance. This includes ensuring that data can be found using a central data catalog, implementing end-to-end user management that takes GDPR compliance and other access restrictions into account, and providing data via interfaces for downstream data solutions such as machine learning and artificial intelligence (AI).

Data Hub

A data hub acts as a central junction in the data management landscape of an organisation, bridging the gap between raw data sources and various use cases that demand refined, reliable, and actionable information. Imagine it as the heart of the data ecosystem, pumping data throughout the organisation in a way that's both controlled and efficient.

1. Integration of Diverse Data Sources: Data hubs integrate data from multiple sources—be it internal databases, external APIs, cloud services, or IoT devices—into a single, cohesive environment. This unification allows for more straightforward management and access, eliminating silos and facilitating a more holistic view of the organisation's data landscape.
2. Data Cleaning and Transformation: Before data can be used effectively, it often needs to be cleaned, transformed, and standardised. The data hub undertakes this process, ensuring that data from disparate sources becomes compatible and useful across different applications and use cases.
3. Governance and Compliance: Data hubs enforce data governance policies and ensure compliance with relevant regulations. By centralising data management, organisations can more easily implement rules regarding data quality, privacy, security, and usage, ensuring that all data is handled appropriately and ethically.
4. Facilitation of Analytics and Reporting: With a central repository of clean, reliable data, organisations can more effectively carry out analytics, generate reports, and derive insights. This enables data-driven decision-making and supports various use cases, from operational efficiency and customer experience improvement to strategic planning and risk management.
5. Data Accessibility and Sharing: By serving as a central point for data, a data hub simplifies access for users across the organisation, tailored to their specific needs and permissions. This democratisation of data empowers teams and individuals, enabling them to leverage data for their specific use cases without needing to navigate complex IT infrastructure.
6. Scalability and Flexibility: As organisations grow and their data needs evolve, data hubs provide a scalable and flexible framework that can adapt to changing requirements. This adaptability ensures that as new data sources are added and new use cases emerge, the data hub can continue to serve as an effective link between data and its applications.

In essence, a data hub streamlines the path from raw data to actionable insights, ensuring that data is not just collected but also transformed into a valuable asset that drives business outcomes and supports a wide array of use cases.

Data-Driven Value Generation

Real added value can be generated with the right foundation of data. We support you in data-driven value generation with various workshop formats, dashboarding and BI, machine learning and AI as well as the development of cloud-native, data-centric and Gen AI-based applications. 

Use Case Screening Workshop

Our Use Case Screening Workshop includes the identification and evaluation of business pain points and use cases that can be solved by advanced analytics. We then create a list of the top three data-driven use cases, taking into account your return on investment (ROI), complexity and development effort.

Dashboarding and Business Intelligence (BI)

We implement business intelligence solutions and develop dashboards using native BI services. In this way, we lay the foundation for meaningful data analyses. The insights gained can be integrated into applications or dashboards or made accessible via web interfaces.

Machine Learning (ML) + Artificial Intelligence (AI)

We leverage Google Cloud's no/low-code AI services to implement data-driven use cases. These solutions integrate seamlessly with the Data Foundation and Google Cloud Services, making the results accessible to applications and other downstream solutions. Optionally, we offer support in the creation of individual machine learning models, which can be provided via Machine Learning Operations (MLOps).

Cloud Native Software Development

We design, manage, optimize and develop data-centric applications on the Google Cloud for you, with a special focus on Machine Learning (ML) and Artificial Intelligence (AI). This includes creating user-friendly and comprehensive web front-ends as well as cross-platform mobile apps to harness the value of your data.

UI/UX Design

Through a sophisticated UI/UX design approach, we ensure the development of outstanding digital experiences that are appreciated by users and drive the success of your data-centric applications.

Generative AI (GenAI)

GenAI Kickstarter Workshop

Discover Generative AI (GenAI) from a practical perspective and evaluate its applicability in your business context. We help you to outline development opportunities and provide guidance for the deployment of GenAI use cases with measurable business value.

Generative AI use case development

Our services include the development of GenAI solutions using no/low-code approaches and the optimization of existing GenAI models through fine-tuning. For this we rely on the GenAI portfolio from Google Cloud such as Gemini and Vertex AI.